MetaInsight

An R shiny app for network meta-analysis

Simon Smart

14th November 2025

Network meta-analysis can help determine the ‘best’ treatment option

Complex reviews require extensive statistical and programming knowledge

  • BUGSnet, bnma, coda, gemtc, meta, metafor, netmeta
  • Some dependent on JAGS which may be hard to install
  • Require data in different formats and use different terminology

Shiny removes barriers for accessing cutting-edge methods

  • MetaInsight was launched in 2019 to make NMA more accessible
  • Originally developed by statisticians, but increasingly in collaboration with developers

The ‘black box’ nature of apps can limit uptake

  • {shinyscholar} was forked from {wallace} to make development of reproducible apps easier
  • Convert core functionality into functions and package
  • App becomes an interface to the functions, dealing with interactivity

Hex logo of shinyscholar showing a black and white mortar cap

Reproducibility relies on a strict structure

  • Each module has an id made up of the component and module e.g. summary_network
  • The module calls a synonymous function summary_network()
  • Input values are stored in common$meta$summary_network$<input id>
  • Values are knitted into an .Rmd chunk and combined to create a .qmd

Reproducibility relies on a strict structure

```{asis, echo = {{summary_network_knit}, eval = {{summary_network_knit}}, include = {{summary_network_knit}}}}
### Display the networks for the original data and data with excluded studies.
{r,  results = 'asis'}
```
```{r, echo = {{summary_network_knit}, include = {{summary_network_knit}}}}
summary_network(frequentist_all, 
                bugsnet_all, 
                {{summary_network_style}}, 
                {{summary_network_label_all}}, 
                "Network plot of all studies")
```


### Display the networks for the original data and data with excluded studies.

```{r, results = 'asis'}
summary_network(frequentist_all, 
                bugsnet_all, 
                "netplot", 
                1, 
                "Network plot of all studies")
```

Reproducibility also enables improved reporting

  • Use as the basis for writing a publication
  • Rendered in the app to produce an html report

Incorporating risk of bias scores improves sensitivity analyses

  • MetaInsight enables sensitivity analyses by excluding studies
  • During reviews, risk of bias information can be collected e.g.
    • Randomisation, blinding, missing data
  • Users can now upload these scores and use them to guide sensitivity analyses

Integration with CiNEMA helps to evaluate confidence in findings

  • Uses risk of bias scores for studies to evaluate evidence for treatments

Acknowledgments

  • Naomi Bradbury, Ryan Field, Tom Morris, Clareece Nevill, Janion Nevill, Alex Sutton, Nicola Cooper
  • Wellcome
  • NIHR
  • Chan Zuckerburg Initiative
  • More apps